5 papers
Performance of morphological classifiers for galaxy mergers compared to current machine learning methods
Aidan P. Cotter, William J. pearson, Subhrata Dey +3
Aims. Non-parametric morphological statistics can be used for efficient classification of galaxy mergers. This work aims to compare the performance of morphological merger classifi…
The TNG50-SKIRT Atlas: Multi-wavelength nonparametric galaxy morphology
Sena Bokona Tulu, Maarten Baes, Angelos Nersesian +5
Context: Galaxy morphology is a fundamental property to describe galaxy evolution. However, the observed morphology of a particular galaxy may depend on the observed wavelength. Ai…
statmorph-lsst: Quantifying and correcting morphological biases in galaxy surveys
Elizaveta Sazonova, Cameron R. Morgan, Michael Balogh +16
Quantitative morphology provides a key probe of galaxy evolution across cosmic time and environments. However, these metrics can be biased by changes in imaging quality - resolutio…
AGN -- host galaxy photometric decomposition using a fast, accurate and precise deep learning approach
Berta Margalef-Bentabol, Lingyu Wang, Antonio La Marca +1
Identifying active galactic nuclei (AGN) is extremely important for understanding galaxy evolution and its connection with the assembly of supermassive black holes (SMBH). With the…
Classifying merger stages with adaptive deep learning and cosmological hydrodynamical simulations
Rosa de Graaff, Berta Margalef-Bentabol, Lingyu Wang +4
Hierarchical merging of galaxies plays an important role in galaxy formation and evolution. Mergers could trigger key evolutionary phases such as starburst activities and active ac…